Background: Molecular docking is probably the most popular and profitable approach in
computer-aided drug design, being the staple technique for predicting the binding mode of bioactive
compounds and for performing receptor-based virtual screening studies. The growing attention received
by docking, as well as the need for improving its reliability in pose prediction and virtual screening
performance, has led to the development of a wide plethora of new docking algorithms and scoring
functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in
terms of reliability and accuracy or demonstrating to be generally suitable for all kind of protein targets.
Methods: In this context, consensus docking approaches are taking hold in computer-aided drug design.
These computational protocols consist in docking ligands using multiple docking methods and then
comparing the binding poses predicted for the same ligand by the different methods. This analysis is
usually carried out calculating the root-mean-square deviation among the different docking results obtained
for each ligand, in order to identify the number of docking methods producing the same binding
Results: The consensus docking approaches demonstrated to improve the quality of docking and virtual
screening results compared to the single docking methods. From a qualitative point of view, the improvement
in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a
high number of docking methods, whereas with regards to virtual screening studies, high hit rates were
obtained by prioritizing the compounds showing a high level of pose consensus.
Conclusion: In this review, we provide an overview of the results obtained from the performance assessment
of various consensus docking protocols and we illustrate successful case studies where consensus
docking has been applied in virtual screening studies.
Keywords: consensus docking, molecular modeling, virtual screening, docking reliability, computer-aided drug design
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